Estimation of walking direction for pedestrian path prediction from moving vehicle

This paper presents a method for estimating of walking direction for pedestrian path prediction. Pedestrian intending to laterally cross the street is observed by images which captured from a monocular camera mounted on the vehicle. The positional information of object is obtained by projecting the centroid of bounding box in the ground plane. Then, dependency between the real worlds, global coordinates and the position of object in the image is explained. The way to find the estimated distance of a pedestrian is introduced by using its position on the image. Walking direction of pedestrian is determined by concatenating consecutive frames. For pedestrian path prediction, Kalman filter (KF), interacting multiple models (IMM) and probabilistic hierarchical trajectory matching are evaluated. In experiments, publicly dataset is used. Distance of object (pedestrian), walking direction and accuracy of path prediction are evaluated. The largest distance error is 1.35 meters from the vehicle, walking direction accuracy is 97.50% and path prediction error is 0.08 meters.